标签：ble amp through transform streaming data exist query 实践
1 基本的Select 操作
SELECT [ALL | DISTINCT] select_expr, select_expr, ...
FROM table_reference
[WHERE where_condition]
[GROUP BY col_list [HAVING condition]]
[ CLUSTER BY col_list
| [DISTRIBUTE BY col_list] [SORT BY| ORDER BY col_list]
[LIMIT number]
•使用ALL和DISTINCT选项区分对重复记录的处理。默认是ALL，表示查询所有记录。DISTINCT表示去掉重复的记录
•Where 条件
•类似我们传统SQL的where 条件
•目前支持 AND,OR ,0.9版本支持between
•IN, NOT IN
•不支持EXIST ,NOT EXIST
ORDER BY与SORT BY的不同
•ORDER BY 全局排序，只有一个Reduce任务
•SORT BY 只在本机做排序
Limit
•Limit 可以限制查询的记录数
SELECT * FROM t1 LIMIT 5
•实现Top k 查询
•下面的查询语句查询销售记录最大的 5 个销售代表。
SET mapred.reduce.tasks = 1
SELECT * FROM test SORT BY amount DESC LIMIT 5
•REGEX Column Specification
SELECT 语句可以使用正则表达式做列选择，下面的语句查询除了 ds 和 hr 之外的所有列：
SELECT `(ds|hr)?+.+` FROM test
例如
按先件查询
hive> SELECT a.foo FROM invites a WHERE a.ds=‘<DATE>‘;
将查询数据输出至目录：
hive> INSERT OVERWRITE DIRECTORY ‘/tmp/hdfs_out‘ SELECT a.* FROM invites a WHERE a.ds=‘<DATE>‘;
将查询结果输出至本地目录：
hive> INSERT OVERWRITE LOCAL DIRECTORY ‘/tmp/local_out‘ SELECT a.* FROM pokes a;
选择所有列到本地目录 ：
hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a;
hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a WHERE a.key < 100;
hive> INSERT OVERWRITE LOCAL DIRECTORY ‘/tmp/reg_3‘ SELECT a.* FROM events a;
hive> INSERT OVERWRITE DIRECTORY ‘/tmp/reg_4‘ select a.invites, a.pokes FROM profiles a;
hive> INSERT OVERWRITE DIRECTORY ‘/tmp/reg_5‘ SELECT COUNT(1) FROM invites a WHERE a.ds=‘<DATE>‘;
hive> INSERT OVERWRITE DIRECTORY ‘/tmp/reg_5‘ SELECT a.foo, a.bar FROM invites a;
hive> INSERT OVERWRITE LOCAL DIRECTORY ‘/tmp/sum‘ SELECT SUM(a.pc) FROM pc1 a;
将一个表的统计结果插入另一个表中：
hive> FROM invites a INSERT OVERWRITE TABLE events SELECT a.bar, count(1) WHERE a.foo > 0 GROUP BY a.bar;
hive> INSERT OVERWRITE TABLE events SELECT a.bar, count(1) FROM invites a WHERE a.foo > 0 GROUP BY a.bar;
JOIN
hive> FROM pokes t1 JOIN invites t2 ON (t1.bar = t2.bar) INSERT OVERWRITE TABLE events SELECT t1.bar, t1.foo, t2.foo;
将多表数据插入到同一表中：
FROM src
INSERT OVERWRITE TABLE dest1 SELECT src.* WHERE src.key < 100
INSERT OVERWRITE TABLE dest2 SELECT src.key, src.value WHERE src.key >= 100 and src.key < 200
INSERT OVERWRITE TABLE dest3 PARTITION(ds=‘2008-04-08‘, hr=‘12‘) SELECT src.key WHERE src.key >= 200 and src.key < 300
INSERT OVERWRITE LOCAL DIRECTORY ‘/tmp/dest4.out‘ SELECT src.value WHERE src.key >= 300;
将文件流直接插入文件：
hive> FROM invites a INSERT OVERWRITE TABLE events SELECT TRANSFORM(a.foo, a.bar) AS (oof, rab) USING ‘/bin/cat‘ WHERE a.ds > ‘2008-08-09‘;
This streams the data in the map phase through the script /bin/cat (like hadoop streaming). Similarly - streaming can be used on the reduce side (please see the Hive Tutorial or examples)
2. 基于Partition的查询
•一般 SELECT 查询会扫描整个表，使用 PARTITIONED BY 子句建表，查询就可以利用分区剪枝（input pruning）的特性
•Hive 当前的实现是，只有分区断言出现在离 FROM 子句最近的那个WHERE 子句中，才会启用分区剪枝
3.Join
Syntax
join_table:
table_reference JOIN table_factor [join_condition]
| table_reference {LEFT|RIGHT|FULL} [OUTER] JOIN table_reference join_condition
| table_reference LEFT SEMI JOIN table_reference join_condition
table_reference:
table_factor
| join_table
table_factor:
tbl_name [alias]
| table_subquery alias
| ( table_references )
join_condition:
ON equality_expression ( AND equality_expression )*
equality_expression:
expression = expression
•Hive 只支持等值连接（equality joins）、外连接（outer joins）和（left semi joins）。Hive 不支持所有非等值的连接，因为非等值连接非常难转化到 map/reduce 任务
•LEFT，RIGHT和FULL OUTER关键字用于处理join中空记录的情况
•LEFT SEMI JOIN 是 IN/EXISTS 子查询的一种更高效的实现
•join 时，每次 map/reduce 任务的逻辑是这样的：reducer 会缓存 join 序列中除了最后一个表的所有表的记录，再通过最后一个表将结果序列化到文件系统
•实践中，应该把最大的那个表写在最后
join 查询时，需要注意几个关键点
只支持等值join
•SELECT a.* FROM a JOIN b ON (a.id = b.id)
•SELECT a.* FROM a JOIN b
ON (a.id = b.id AND a.department = b.department)
•可以 join 多于 2 个表，例如
SELECT a.val, b.val, c.val FROM a JOIN b
ON (a.key = b.key1) JOIN c ON (c.key = b.key2)
•如果join中多个表的 join key 是同一个，则 join 会被转化为单个 map/reduce 任务
LEFT，RIGHT和FULL OUTER
例子
•SELECT a.val, b.val FROM a LEFT OUTER JOIN b ON (a.key=b.key)
•如果你想限制 join 的输出，应该在 WHERE 子句中写过滤条件——或是在 join 子句中写
•容易混淆的问题是表分区的情况
• SELECT c.val, d.val FROM c LEFT OUTER JOIN d ON (c.key=d.key)
WHERE a.ds=‘2010-07-07‘ AND b.ds=‘2010-07-07‘
•如果 d 表中找不到对应 c 表的记录，d 表的所有列都会列出 NULL，包括 ds 列。也就是说，join 会过滤 d 表中不能找到匹配 c 表 join key 的所有记录。这样的话，LEFT OUTER 就使得查询结果与 WHERE 子句无关
•解决办法
•SELECT c.val, d.val FROM c LEFT OUTER JOIN d
ON (c.key=d.key AND d.ds=‘2009-07-07‘ AND c.ds=‘2009-07-07‘)
LEFT SEMI JOIN
•LEFT SEMI JOIN 的限制是， JOIN 子句中右边的表只能在 ON 子句中设置过滤条件，在 WHERE 子句、SELECT 子句或其他地方过滤都不行
•
•SELECT a.key, a.value
FROM a
WHERE a.key in
(SELECT b.key
FROM B);
可以被重写为：
SELECT a.key, a.val
FROM a LEFT SEMI JOIN b on (a.key = b.key)
UNION ALL
•用来合并多个select的查询结果，需要保证select中字段须一致
•select_statement UNION ALL select_statement UNION ALL select_statement ...
009-Hadoop Hive sql语法详解4-DQL 操作:数据查询SQL
标签：ble amp through transform streaming data exist query 实践
原文地址：http://www.cnblogs.com/bjlhx/p/6946202.html
